Consumers often prefer to know whether and when business establishments are open for business, to allow them organize and plan their own schedules. There is no central repository, however, as to when all businesses are open.
When a credit card is used to pay for a product, the merchant submits a request to an acquirer bank. The acquirer bank then sends a request to an issuer bank that authorizes or declines the transaction. If the transaction is approved, the issuer bank provides an authorization code to the acquirer bank, which notifies the merchant to complete the transaction. Each request and authorization involved in this process includes data about the merchant, the consumer, and the transaction. For example, credit card authorizations may have time stamps, a merchant identifier, a transaction amount, and an account number, among other things. Therefore, credit card authorizations may be used to make inferences about the merchant, the consumer, or the transaction. Particularly nowadays, when millions of credit card transactions are recorded every day, statistical methods can be used to analyze credit card authorization data, make accurate inferences, and observe trends.
Credit card authorizations are normally generated when a merchant is open for operation and serving customers. Typically, merchants submit the credit card authorization requests to the acquirer bank concurrently with a consumer making a purchase. Therefore, credit card authorizations can be used to infer whether the merchant is serving customers and is thus “open.” For example, a restaurant may normally issue multiple credit card authorization requests between 11 am and 1 pm when it serves customers during lunch. Thus, it is possible to infer that the restaurant is open between 11 am and 1 pm, based on credit card authorization requests. On the other hand, the same restaurant may not issue any credit card authorization requests between 1 am and 2 am. Thus, it is possible to infer that the restaurant is “closed” between 1 am and 2 am. Therefore, analysis of credit card authorization data can be used to predict a merchant's hours of operation.
However, making accurate predictions of merchant's hours of operation based on credit card authorization request data alone may be challenging for several reasons. First, there may be an imperfect correlation between credit card authorization data and hours of operation. For example, a restaurant may open at 10 am but only start issuing credit card authorizations at 10:30 am, when the first customer finishes his or her meal and pays. In this example, the correlation between hours of operation and authorization requests is offset and may lead to prediction errors. Second, data repositories of credit card authorizations may store millions of authorizations per day. The large quantity and variety of authorization formats and merchant practices may make it difficult to effectively process the authorization data. Third, the correlations between credit card authorizations and hours of operation can be dynamic and may be influenced by externalities. For example, the correlation between authorizations and hours of operation may be influenced by merchant location, season, and/or business type. For instance, a merchant may have summer hours of operation that are different to the winter hours of operation. These are some of the difficulties that make prediction of hours of operation challenging, but other variables also affect correlations and predictions.
The disclosed machine learning artificial intelligence system and modeling methods address one or more of the problems set forth above and/or other problems in the prior art.